1. Field of the Invention
The present invention relates to a failure analysis method that classifies wafer failure distributions in units of wafers and also in units of lot.
2. Description of the Related Art
In order to enhance LSI yields, clarification of the cause of deterioration in the yields, early identification of a problematic LSI manufacturing process, a problematic manufacturing device, or a problematic design condition, and improvement thereof are critical. As LSI miniaturization progresses, various failures that reduce the LSI yields have become obvious. Failure information including the test results of LSI wafers, in particular, memory products, which have been tested by a tester immediately after completion of a wafer process, is mapped into a fail bit map (FBM). Analysis of the FBM and a defect map of wafer defects developed inline during the manufacturing process is critical to improvement of preventing failures.
Wafer failure distributions of the FBM and the defect map can be categorized as either random distribution failures or clustering distribution failures. It is thought that clustering distribution represents a systematic factor due to a manufacturing process or a manufacturing device. The clustering distribution is a significant cause of reduction in the LSI yields.
Therefore, detection of clustering failures from the failure distribution is the first step to identify the cause of failures; a detection method thereof has been proposed.
Failures due to a manufacturing process or a manufacturing device develop manufacturing process- or manufacturing device-specific wafer failure distribution. Therefore, failure pattern analysis of the clustering distribution may be thought of as a clue to identify the cause of failure.
As the second step to identify the cause of failure, the clustering distribution is subjected to failure pattern analysis. This is capable of identifying a bit failure, a row failure, and a column failure according to which the FBMs for memory products may be microscopically classified, an open/short interconnects that may be a physical cause thereof, and a layer damaged therefrom. In addition, macroscopic classification of FBM distribution of wafers is made to identify the cause of failure. It has been reported that the waveform of the probability distribution function in terms of the distance between failure bits in the FBM can be classified according to seven certain failure modes.
It has been reported that classification according to fifty-five failure modes can be made by combining the failure distribution in a wafer resulting from macroscopically classifying the FBM, and microscopic classification thereof. Failure patterns have been classified using an artificial neural network, with the FBM as a picture image.
Alternatively, a fail bit count (FBC) data method that counts the number of failure bits for every minutely divided section in the memory products has been proposed.
It has been reported that an usage of FBC data allows classification according to various failure modes, such as lithography-caused failure.